_base_ = [
    # '../_base_/schedules/schedule_1x.py', 
    '../_base_/default_runtime.py'
]

dataset_type = 'CocoDataset'
classes = (
    'weiguoban',
    'xidong',
    'zangwu',
    'shaoxi',
    'lianxi',
    'jiaochang',
    'yiwu',
    'other'
 )

num_classes = len(classes)

norm_cfg = dict(type='BN', requires_grad=True)
# norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='ATSS',
    backbone=dict(
        type='ResNeXt',
        depth=101,
        groups=64,
        base_width=4,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=norm_cfg,
        style='pytorch',
        init_cfg=dict(
            type='Pretrained', checkpoint='pretrain_weights/resnext101_64x4d-ee2c6f71.pth')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=5),
    bbox_head=dict(
        type='ATSSHead',
        num_classes=num_classes,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            ratios=[1.0],
            octave_base_scale=8,
            scales_per_octave=1,
            strides=[8, 16, 32, 64, 128]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[.0, .0, .0, .0],
            target_stds=[0.1, 0.1, 0.2, 0.2]),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
        loss_centerness=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(type='ATSSAssigner', topk=9),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.001,
        nms=dict(type='nms', iou_threshold=0.6),
        max_per_img=100))


img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='Resize',
        img_scale=[(416, 416)],
        multiscale_mode='value',
        keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=[0.2,0.2,0.2], direction=['horizontal', 'vertical', 'diagonal']),
    dict(type='BrightnessTransform', level=5, prob=0.5),
    dict(type='ContrastTransform', level=5, prob=0.5),
    dict(type='RandomShift', shift_ratio=0.5),
    dict(type='MinIoURandomCrop', min_ious=(0.5, 0.7, 0.9), min_crop_size=0.8),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=[(416, 416)],
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]


train_dataset = dict(
            type=dataset_type,
            img_prefix= 'data/coco/train2017/',
            classes=classes,
            ann_file='data/coco/annotations/instances_train2017.json',
            pipeline=train_pipeline,
            filter_empty_gt=False,
        )

test_dataset = dict(
        type=dataset_type,
        img_prefix= 'data/coco/train2017/',
        classes=classes,
        ann_file='data/coco/annotations/instances_train2017.json',
        pipeline=test_pipeline,
        filter_empty_gt=False)

train_concat_dataset = dict(
        type='ConcatDataset',
        datasets=[])

test_concat_dataset = dict(
        type='ConcatDataset',
        datasets=[])

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=train_concat_dataset,
    val=test_concat_dataset,
    test=test_concat_dataset)

# optimizer
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=1000,
    warmup_ratio=1.0 / 10,
    min_lr_ratio=1e-5)
runner = dict(type='EpochBasedRunner', max_epochs=36)

evaluation = dict(interval=10, metric='bbox')
checkpoint_config = dict(interval=5)

find_unused_parameters=True
log_config = dict(interval=20)
# custom hooks
custom_hooks = [dict(type='SetEpochInfoHook')]

# resume_from = '/home/junlin-wen/userdata/package/0guanjie/anomaly_detection/workdirs/zhuban_defect/AD_gj_zhuban_defect_1012/epoch_24.pth'